Overview

Dataset statistics

Number of variables3
Number of observations86
Missing cells3
Missing cells (%)1.2%
Duplicate rows1
Duplicate rows (%)1.2%
Total size in memory2.3 KiB
Average record size in memory27.5 B

Variable types

Text1
Numeric2

Dataset

Description예금보험공사 고유계정(예금보험기금 및 공사회계 통합) 재무상태표에 대한 데이터로 당기 및 전기의 계정과목, 금액에 대한 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15090067/fileData.do

Alerts

Dataset has 1 (1.2%) duplicate rowsDuplicates
제27(당)기 is highly overall correlated with 제26(전)기High correlation
제26(전)기 is highly overall correlated with 제27(당)기High correlation
과목 has 1 (1.2%) missing valuesMissing
제27(당)기 has 1 (1.2%) missing valuesMissing
제26(전)기 has 1 (1.2%) missing valuesMissing
제27(당)기 has 2 (2.3%) zerosZeros
제26(전)기 has 6 (7.0%) zerosZeros

Reproduction

Analysis started2023-12-12 15:13:56.717443
Analysis finished2023-12-12 15:13:57.556680
Duration0.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

과목
Text

MISSING 

Distinct76
Distinct (%)89.4%
Missing1
Missing (%)1.2%
Memory size820.0 B
2023-12-13T00:13:57.736566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length7.3058824
Min length2

Characters and Unicode

Total characters621
Distinct characters95
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)83.5%

Sample

1st rowI.유동자산
2nd row현금및현금성자산
3rd row현금성자산
4th row(정부보조금)
5th row유동금융자산
ValueCountFrequency (%)
대손충당금 4
 
4.7%
감가상각누계액 4
 
4.7%
사채할증발행차금 2
 
2.4%
현재가치할인차금 2
 
2.4%
사채할인발행차금 2
 
2.4%
정부보조금 2
 
2.4%
유동금융부채 1
 
1.2%
유동비금융부채 1
 
1.2%
유동성사채 1
 
1.2%
현재가치할인차금(유동성장기차입금 1
 
1.2%
Other values (65) 65
76.5%
2023-12-13T00:13:58.123615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
금 46
 
7.4%
채 30
 
4.8%
기 29
 
4.7%
유 26
 
4.2%
동 24
 
3.9%
자 23
 
3.7%
산 20
 
3.2%
부 17
 
2.7%
비 17
 
2.7%
( 15
 
2.4%
Other values (85) 374
60.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 572
92.1%
Open Punctuation 15
 
2.4%
Close Punctuation 15
 
2.4%
Uppercase Letter 9
 
1.4%
Other Punctuation 6
 
1.0%
Dash Punctuation 3
 
0.5%
Connector Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
금 46
 
8.0%
채 30
 
5.2%
기 29
 
5.1%
유 26
 
4.5%
동 24
 
4.2%
자 23
 
4.0%
산 20
 
3.5%
부 17
 
3.0%
비 17
 
3.0%
융 14
 
2.4%
Other values (79) 326
57.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Uppercase Letter
ValueCountFrequency (%)
I 9
100.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 572
92.1%
Common 40
 
6.4%
Latin 9
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
금 46
 
8.0%
채 30
 
5.2%
기 29
 
5.1%
유 26
 
4.5%
동 24
 
4.2%
자 23
 
4.0%
산 20
 
3.5%
부 17
 
3.0%
비 17
 
3.0%
융 14
 
2.4%
Other values (79) 326
57.0%
Common
ValueCountFrequency (%)
( 15
37.5%
) 15
37.5%
. 6
 
15.0%
- 3
 
7.5%
_ 1
 
2.5%
Latin
ValueCountFrequency (%)
I 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 572
92.1%
ASCII 49
 
7.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
금 46
 
8.0%
채 30
 
5.2%
기 29
 
5.1%
유 26
 
4.5%
동 24
 
4.2%
자 23
 
4.0%
산 20
 
3.5%
부 17
 
3.0%
비 17
 
3.0%
융 14
 
2.4%
Other values (79) 326
57.0%
ASCII
ValueCountFrequency (%)
( 15
30.6%
) 15
30.6%
I 9
18.4%
. 6
 
12.2%
- 3
 
6.1%
_ 1
 
2.0%

제27(당)기
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct77
Distinct (%)90.6%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean1.7492004 × 1012
Minimum-1.33695 × 1013
Maximum1.77683 × 1013
Zeros2
Zeros (%)2.3%
Negative15
Negative (%)17.4%
Memory size906.0 B
2023-12-13T00:13:58.258471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.33695 × 1013
5-th percentile-2.2342453 × 1010
Q11.5611538 × 108
median1.0825157 × 1010
Q31.52832 × 1012
95-th percentile1.142844 × 1013
Maximum1.77683 × 1013
Range3.11378 × 1013
Interquartile range (IQR)1.5281639 × 1012

Descriptive statistics

Standard deviation4.4512107 × 1012
Coefficient of variation (CV)2.5447117
Kurtosis5.35829
Mean1.7492004 × 1012
Median Absolute Deviation (MAD)1.2318048 × 1010
Skewness1.6139812
Sum1.4868204 × 1014
Variance1.9813277 × 1025
MonotonicityNot monotonic
2023-12-13T00:13:58.392883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-98721015600 2
 
2.3%
11308600000000 2
 
2.3%
7373227608 2
 
2.3%
0 2
 
2.3%
17768300000000 2
 
2.3%
2575377365 2
 
2.3%
156115382 2
 
2.3%
10878889701 2
 
2.3%
13005400000000 1
 
1.2%
6576799453 1
 
1.2%
Other values (67) 67
77.9%
ValueCountFrequency (%)
-13369500000000 1
1.2%
-98721015600 2
2.3%
-24781066735 1
1.2%
-24443709756 1
1.2%
-13937424302 1
1.2%
-1787963617 1
1.2%
-1780237410 1
1.2%
-1704687312 1
1.2%
-1492890824 1
1.2%
-931520268 1
1.2%
ValueCountFrequency (%)
17768300000000 2
2.3%
14233200000000 1
1.2%
13005400000000 1
1.2%
11458400000000 1
1.2%
11308600000000 2
2.3%
11209800000000 1
1.2%
7107800000000 1
1.2%
6558420000000 1
1.2%
4762870000000 1
1.2%
3988080000000 1
1.2%

제26(전)기
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct73
Distinct (%)85.9%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean1.6416414 × 1012
Minimum-1.36897 × 1013
Maximum1.64576 × 1013
Zeros6
Zeros (%)7.0%
Negative14
Negative (%)16.3%
Memory size906.0 B
2023-12-13T00:13:58.530323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.36897 × 1013
5-th percentile-2.134948 × 1010
Q173560618
median7.3732276 × 109
Q31.34229 × 1012
95-th percentile9.446044 × 1012
Maximum1.64576 × 1013
Range3.01473 × 1013
Interquartile range (IQR)1.3422164 × 1012

Descriptive statistics

Standard deviation4.1438277 × 1012
Coefficient of variation (CV)2.5241979
Kurtosis5.6424059
Mean1.6416414 × 1012
Median Absolute Deviation (MAD)9.3139445 × 109
Skewness1.3350839
Sum1.3953952 × 1014
Variance1.7171308 × 1025
MonotonicityNot monotonic
2023-12-13T00:13:58.665562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
7.0%
-51906044850 2
 
2.3%
8621940000000 2
 
2.3%
7373227608 2
 
2.3%
2544647224 2
 
2.3%
16457600000000 2
 
2.3%
120779310 2
 
2.3%
16687172070 2
 
2.3%
11019200000000 1
 
1.2%
47276273351 1
 
1.2%
Other values (63) 63
73.3%
ValueCountFrequency (%)
-13689700000000 1
1.2%
-51906044850 2
2.3%
-33615339086 1
1.2%
-24594674341 1
1.2%
-8368703960 1
1.2%
-1787811256 1
1.2%
-1442734697 1
1.2%
-859187553 1
1.2%
-619885072 1
1.2%
-121658665 1
1.2%
ValueCountFrequency (%)
16457600000000 2
2.3%
14499900000000 1
1.2%
11019200000000 1
1.2%
9652070000000 1
1.2%
8621940000000 2
2.3%
8570040000000 1
1.2%
7887590000000 1
1.2%
7644990000000 1
1.2%
5438430000000 1
1.2%
5109640000000 1
1.2%

Interactions

2023-12-13T00:13:57.091635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:13:56.858950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:13:57.216358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:13:56.963964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:13:58.759104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과목제27(당)기제26(전)기
과목1.0001.0001.000
제27(당)기1.0001.0000.983
제26(전)기1.0000.9831.000
2023-12-13T00:13:58.840306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제27(당)기제26(전)기
제27(당)기1.0000.977
제26(전)기0.9771.000

Missing values

2023-12-13T00:13:57.346877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:13:57.429250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T00:13:57.509029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

과목제27(당)기제26(전)기
0I.유동자산1300540000000011019200000000
1현금및현금성자산1712656278623616419610
2현금성자산1724822145123738078275
3(정부보조금)-121658665-121658665
4유동금융자산114584000000009652070000000
5유동당기손익-공정가치측정금융자산29968700000062145102620
6유동기타포괄손익-공정가치측정금융자산39880800000001882130000000
7단기대여금6281598758962815987589
8(대손충당금)00
9단기금융상품71078000000007644990000000
과목제27(당)기제26(전)기
76비유동비금융부채73732276087373227608
77기타비유동비금융부채73732276087373227608
78부채총계65584200000007887590000000
79I.잉여금113086000000008621940000000
80미처분잉여금113086000000008621940000000
81II.기타자본구성요소-98721015600-51906044850
82기타포괄손익누계액-98721015600-51906044850
83자본총계112098000000008570040000000
84부채및자본총계1776830000000016457600000000
85<NA><NA><NA>

Duplicate rows

Most frequently occurring

과목제27(당)기제26(전)기# duplicates
0(대손충당금)002